





'''simple docstring'''

import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import torch






class   UpperCAmelCase      (			TensorFormatter[Mapping, """torch.Tensor""", Mapping]							):
 def __init__(							self :int		,			lowercase_ :Optional[Any]=None		,			**lowercase_ :str				)->				Optional[Any]:
     super().__init__(features=lowercase_				)
     A__							=     torch_tensor_kwargs
     import torch  # noqa import torch at initialization
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Union[str, Any]				)->				Dict:
     import torch

     if isinstance(lowercase_		,			lowercase_				) and column:
         if all(
             isinstance(lowercase_		,			torch.Tensor				) and x.shape == column[0].shape and x.dtype == column[0].dtype
             for x in column				):
             return torch.stack(lowercase_				)
     return column
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :Dict				)->				List[str]:
     import torch

     if isinstance(lowercase_		,			(str, bytes, type(lowercase_				))				):
         return value
     elif isinstance(lowercase_		,			(np.character, np.ndarray)				) and np.issubdtype(value.dtype		,			np.character				):
         return value.tolist()

     A__							=     {}

     if isinstance(lowercase_		,			(np.number, np.ndarray)				) and np.issubdtype(value.dtype		,			np.integer				):
         A__							=     {"dtype": torch.intaa}
     elif isinstance(lowercase_		,			(np.number, np.ndarray)				) and np.issubdtype(value.dtype		,			np.floating				):
         A__							=     {"dtype": torch.floataa}
     elif config.PIL_AVAILABLE and "PIL" in sys.modules:
         import PIL.Image

         if isinstance(lowercase_		,			PIL.Image.Image				):
             A__							=     np.asarray(lowercase_				)
     return torch.tensor(lowercase_		,			**{**default_dtype, **self.torch_tensor_kwargs}				)
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :Union[str, Any]				)->				List[str]:
     import torch

     # support for torch, tf, jax etc.
     if hasattr(lowercase_		,			"__array__"				) and not isinstance(lowercase_		,			torch.Tensor				):
         A__							=     data_struct.__array__()
     # support for nested types like struct of list of struct
     if isinstance(lowercase_		,			np.ndarray				):
         if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
             return self._consolidate([self.recursive_tensorize(lowercase_				) for substruct in data_struct]				)
     elif isinstance(lowercase_		,			(list, tuple)				):
         return self._consolidate([self.recursive_tensorize(lowercase_				) for substruct in data_struct]				)
     return self._tensorize(lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :Union[str, Any]				)->				Tuple:
     return map_nested(self._recursive_tensorize		,			lowercase_		,			map_list=lowercase_				)
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :str				)->				List[str]:
     A__							=     self.numpy_arrow_extractor().extract_row(lowercase_				)
     A__							=     self.python_features_decoder.decode_row(lowercase_				)
     return self.recursive_tensorize(lowercase_				)
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :Any				)->				List[Any]:
     A__							=     self.numpy_arrow_extractor().extract_column(lowercase_				)
     A__							=     self.python_features_decoder.decode_column(lowercase_		,			pa_table.column_names[0]				)
     A__							=     self.recursive_tensorize(lowercase_				)
     A__							=     self._consolidate(lowercase_				)
     return column



 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :Optional[int]				)->				Tuple:
     A__							=     self.numpy_arrow_extractor().extract_batch(lowercase_				)
     A__							=     self.python_features_decoder.decode_batch(lowercase_				)
     A__							=     self.recursive_tensorize(lowercase_				)
     for column_name in batch:
         A__							=     self._consolidate(batch[column_name]				)
     return batch




"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
